Abstract
Odometry is one of most-used techniques used in mobile robotics and autonomous vehicles, especially when the indoor navigation is required or when robot or vehicle moves inside the tunnel. The output of the odometer is usually a count of pulses corresponding to the distance run by given wheel. Due to the quantization noise, estimation of the velocity (first derivative of the distance) is challenging. This article is focused on curve-fitting filter used for the speed estimation and optimization of its parameters, considering the physical constraints of the robot, sampling frequency of the system and the quantization step. The paper proposes an empirical formula for estimating the optimal parameters of the curve fitting filter. The optimized filter has been evaluated using both simulation and real experiment and compared with several standard differentiation methods.
Highlights
I N MOBILE robotics, the odometry is one of the key measurement technique being used, when external absolute localization method cannot be used
That the maximal rate of acceleration has the critical impact on the precision and performance of the velocity estimation
Odometry is an essential part of mobile robotics, especially when the robot operates indoors or inside the tunnel, without available signal from the satellite localization
Summary
I N MOBILE robotics, the odometry is one of the key measurement technique being used, when external absolute localization method (e.g. satellite localization) cannot be used. G. automated firefighting vehicles) inside the tunnel. It is usually required to estimate the position of the robot, and its velocity. This can be obtained by differentiation of the odometer readings. There are two approaches: curve fitting and state space estimation. The methods developed for speed estimation can be applied in various applications, where noisy sensor data needs to be differentiated
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